Agentic Conversations (formally mlops.community)

Latest Episodes:

MLOps Meetup #17 // The Challenges of ML Operations & How Hermione Helps Along the Way // Neylson Crepalde Jun 11, 2020

Show notes

MLOps.community meetup #17: a deep dive into the open source ML framework Hermoine built on top of MLflow with Neylson Crepalde

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Key takeaways for attendees:

MLOps problems are dealt with tools but also with processes. Open-source framework Hermione can help in a lot of parts of the operations process

// Abstract:
In Neylson's experience with Machine Learning projects, he has encountered a series of challenges regarding agile processes to build and deploy ML models in a professional cooperative environment that fosters teamwork. While on this journey, Neylson and his team developed some of their own solutions for these challenges. Out of this was the open-source project Hermoine born. Hermoine is a collection of solutions for these specific MLOps problems that were packaged into a library, an ML project structure framework called Hermione.

In this meetup, we talk about these challenges, what they did to overcome them, and how Hermione helped address these different issues along the way. We will also do a demo on how to build an ML project with Hermione.
Check out Hermoine here: https://github.com/a3data/hermione

Neylson Crepalde is a partner and MLOps Tech Lead at A3Data. He holds a PhD in Economic Sociology, a Master's in Sociology of Culture, an MBA in Cultural Management, and a bachelor's degree in Music/Conducting. He is a professor of Machine Learning and Head of the Data Science Department at Izabela Hendrix Methodist Technological University. His main research interests are Machine Learning processes, Politics and Deliberation, Economic Sociology, and Sociology of Education. In his PhD, he has worked with Multilevel Social Network Analysis and Exponential Random Graph Models to understand the social construction of quality in an orchestra’s market.

Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Neyslon on LinkedIn: https://www.linkedin.com/in/neylsoncrepalde/

MLOps Meetup #16 // Venture Capital and Machine Learning Startups with John Spindler Jun 06, 2020

Show notes

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Venture Capital in Machine Learning Startups With John Spindler, CEO of Capital Enterprise.

John Spindler, CEO of Capital Enterprise. We talked about what trends he has been seeing within MLOps, ML companies, and also how he evaluates a deal.

John Spindler has over 15 years of experience as an entrepreneur and business advisor/consultant, and, as well as being responsible for the day-to-day management of Capital Enterprise, he is also a general partner at AI Seed, an early-stage fund that invests in highly talented AI-first companies.

John is on a mission to make it possible for someone moderately intelligent, with a good idea, ambition, and passion, to make it as an entrepreneur.

Join our Slack community: https://go.mlops.community/slack
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Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with John on LinkedIn: https://www.linkedin.com/in/johnbspindler/

MLOps Meetup #15 Scaling Human-in-the-Loop Machine Learning with Robert Munro Jun 04, 2020

Show notes

Human In The Loop Machine Learning and how to scale it with Robert Munro.

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This conversation centered around the components of Human-in-the-Loop Machine Learning systems and the challenges when scaling them. Most machine learning applications learn from human examples. For example, autonomous vehicles know what a pedestrian looks like because people have spent 1000s of hours labeling “pedestrians” in videos; your smart device understands you because people have spent 1000s of hours labeling the intent of speech recordings; and machine translation services work because they are trained on 1000s of sentences that have been manually translated between languages.

If you have a machine learning system that is learning from human feedback in real-time, then there are many components to support and scale, from the machine learning models to the human interfaces and the processes for quality control.

Robert Munro is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image, and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti, and the Amazon, to London, Sydney, and Silicon Valley, in organizations ranging from startups to the United Nations. He has shipped Machine Learning Products at startups and at/with Amazon, Google, IBM & Microsoft.

Robert has published more than 50 papers on Artificial Intelligence and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University. Robert is the author of Human-in-the-Loop Machine Learning (Manning Publications, 2020)

Join our Slack community: https://go.mlops.community/slack
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Robert on LinkedIn: https://www.linkedin.com/in/robertjmunro/

Robert's book on Human in the Loop Machine Learning: https://www.manning.com/books/human-in-the-loop-machine-learning
Blog Post "Active Learning with Pytorch": https://medium.com/pytorch/https-medium-com-robert-munro-active-learning-with-pytorch-2f3ee8ebec

MLOps #14 Kubeflow vs MLflow with Byron Allen May 28, 2020

Show notes

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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The amazing Byron Allen talks to us about why MLflow and Kubeflow are not playing the same game!
ML flow vs Kubeflow is more like comparing apples to oranges, or as he likes to make the analogy, they are both cheese, but one is an all-rounder and the other a high-class delicacy. This can be quite deceiving when analyzing the two. We do a deep dive into the functionalities of both and the pros/cons they have to offer.

Byron is a Senior Consultant at Servian - a data consultancy in Australia that also has a footprint across APAC as well as the UK. Byron is based in the London office, where he helps organizations discover and build competitive advantage through their data. His focus is on client advisory and consulting delivery related to Experiments and ProductionML (i.e., data science, experimental design, ML model development, MLOps).
Byron has written about a wide range of topics, including the divide between data engineer and scientist, the role of ML in the post-COVID world, and Kubeflow vs. MLflow. Check it all out here: https://medium.com/@byron.allen

Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Byron on LinkedIn: https://www.linkedin.com/in/byronaallen/

MLOps meetup #13 // Maximizing Job Opportunities as a Data Scientist on the Market With Anthony Kelly May 27, 2020

Show notes

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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Resume building and Interviewing tips for data scientists and Machine learning engineers.

When on the job hunt, there are some tested tips and tricks that can be applied to your resume and interviews, which will give you a leg up on the rest of the competition.

Anthony Kelly, host of the AI in Action podcast and Executive Search Consultant focused on Machine Learning and Data Science, sat down with us to talk about what some of the best resumes and CVs have in common.

We spoke about optimizing your CV/resume and maximizing opportunities once you land an interview, so you can have the most amount of options to choose from while you are on the market.

Anthony Kelly is a tech recruiter from Dublin, Ireland. Currently, he is the Country Manager for Alldus, an international AI and Data Science Recruitment company. He has been working in recruitment since March 2015, and since joining the recruitment industry, he has been an international top recruiter due to his performance.

Along with the above, he is also the founder of the Berlin AI in Action meetup, which has over 2,000 members. In addition to the meetup, he also has a podcast series called AI in Action and is a Co-Founder of the Berlin AI community Awards.

Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Anthony Kelly on LinkedIn: https://www.linkedin.com/in/anthonypierrekelly/
Check out the AI in Action podcast: https://podtail.com/podcast/ai-in-action-podcast/

MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan May 21, 2020

Show notes

MLOps meetup #12 // What are the advantages for a data scientist to know data engineering?

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What good is learning Data Engineering skills? These days full full-stack is overflowing with all the different things you need to know about, so why learn data Engineering now? Our guest on this meetup will make the case for what the advantages are if you do decide to learn data engineering, and also go into depth on how to do data engineering in the cloud.

Dan Sullivan is a software architect and data scientist with extensive experience in big data, machine learning, data architecture, security, stream processing, and cloud architecture. Dan is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer exam guides, as well as NoSQL for Mere Mortals.

He is also the author of over ten LinkedIn Learning courses on data science, machine learning, SQL, data architecture, and NoSQL. He holds a Ph.D. in genetics, bioinformatics, and computational biology.

Get a copy of his new book here: https://www.wiley.com/en-us/Official+Google+Cloud+Certified+Professional+Data+Engineer+Study+Guide-p-9781119618454

Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Dan Sullivan on LinkedIn: https://www.linkedin.com/in/dansullivanpdx/

MLOps community meetup #11 // Machine Learning at Scale in Mercado Libre with Carlos de la Torre May 16, 2020

Show notes

MLOps community meetup #11 Machine Learning at scale in Mercado Libre with Carlos de la Torre.

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Mercado Libre hosts the largest online commerce and payments ecosystem in Latin America. The IT department built Fury: a PaaS framework for the development and deployment of multi-cloud, multi-technology, microservices. This platform leveraged the growth of the IT area, which now counts ~4000 people.

As such, it lacked support for machine-learning based solutions: an experimentation environment for data scientists, infrastructure and data access support for ETL and models’ training tasks, etc. Therefore, for over a year now, they have been developing Fury Data Apps (FDA). An extension of Fury for the design, experimentation, development, and deployment of machine-learning-based solutions. It is already supporting ~500 users and some high-performance production APIs.

In this meetup, we talk about the main features of the platform, the supporting technology, and why Carlos never accepted my LinkedIn request.
Link to the article Carlos references: https://martinfowler.com/articles/cd4ml.html

Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Carlos on LinkedIn: https://www.linkedin.com/in/carlosdelatorre/
Follow Carlos on Twitter: @py_litox

MLOps.community meetup #9 with Charles Martin - 10 Years Deploying Machine Learning in the Enterprise: The Inside Scoop! May 14, 2020

Show notes

MLOps.community meetup #9 with Charles Martin - 10 years deploying Machine Learning in the Enterprise: The Inside Scoop!

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Why do some machine learning projects succeed while others fall completely? In this discussion, we will discuss the real-world challenges that Enterprises face in deploying ML solutions, focusing on challenges with existing, legacy dev-ops environments and how certain patterns of success emerge to help combat failure.

Dr. Martin runs a boutique consultancy in San Francisco, California, that supports organizations looking to research, build, and deploy data science, machine learning, and AI products. He has worked with clients like eBay, BlackRock, and GoDaddy, as well as widely successful startups such as Aardvark (acquired by Google) and Demand Media (the first public billion-dollar IPO after Google).

He is a world-renowned researcher, collaborating with UC Berkeley on the WeightWatcher project, and has taught at UC Berkeley and Stanford, and spoken at KDD, ICML, etc. He has also provided scientific advisory support to the Anthropocene Institute, advising on areas of nuclear and quantum technologies with an eye toward climate change.

Read more from Charles:

http://calculatedcontent.com/
Join our Slack community: https://go.mlops.community/slack

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Charles on LinkedIn: https://www.linkedin.com/in/charlesmartin14/

MLOps.community #10 - MLOps - The Blind Men and the Elephant with Saurav Chakravorty May 08, 2020

Show notes

Meet up #10 Saurav Chakravorty sat down with us to talk about his vision of how MLOps reflects the old Indian story of blind men and an Elephant.

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As a lead data scientist at Brillo, Saurav has built many MLOps pipelines and has experience using different ML platforms. He comes to talk with us about the difficulties of taking an ML platform from infancy to production and other key factors he has seen within the MLOps space.

Today, data science is a field that is an aggregation of people from various backgrounds - econometrics, statistics, engineering, business analysts, and data engineers. Each of these groups has different expectations from a Machine Learning platform. But, each group faces problems that have some common challenges - improving reproducibility, reducing technical debt, and reducing the time to try new experiments. The challenge before any MLOps system is to create platforms and processes that address the needs of each of these groups.

Saurav is a tinkerer in the Machine Learning world with experience in the design and development of ML applications and processes. In the past few years, he has been focused on improving the processes and tools around the Machine Learning teams. He explores the ideas of Auto ML, ML Ops, and model evaluation. He helps customers adopt and use the best tools and processes that allow them to scale their Data Science or Machine Learning tools. He has development experience in the open stack ML platforms and, of late, the managed ML services from Azure and AWS.

You can read his article about creating your own MLOps pipeline with open source tools here: https://towardsdatascience.com/mlops-reducing-the-technical-debt-of-machine-learning-dac528ef39de

Join our MLOps community Slack:

https://go.mlops.community/slack
Come to our next MLOps meetup: https://tinyurl.com/yajmywre

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Saurav on LinkedIn: https://www.linkedin.com/in/sauravchakravorty/

MLOps.community meetup #8: Optimizing your ML workflow with Kubeflow 1.0 with Josh Bottum VP of Arrikto May 01, 2020

Show notes

Join the Community: ⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠

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LinkedIn, Spotify, Volvo, JP Morgan, and many other market leaders are leveraging Kubeflow to simplify the creation and efficient deployment of Machine Learning models on Kubernetes. This presentation will provide an update on the Kubeflow 1.0 release and review the Community’s best practices to support Critical User Journeys, which optimize ML workflows.

As a data scientist will often need to build (and save) hundreds of variants of their model, this session will provide a deeper dive into how an integrated storage solution simplifies model-building and increases ML productivity.

The presentation will examine how to optimize the daily workflows of data scientists and eliminate complex and time-consuming manual tasks. The talk will also highlight how efficient Kubeflow operations rely on Kubernetes storage primitives, such as Dynamic Volume Provisioning, Persistent Volumes, and StatefulSets. This integrated solution simplifies the configuration, operations, and data protection for Kubeflow and generic K8S stateful apps in production-grade, multi-user environments.

Bio:

Josh Bottum is a Kubeflow Community Product Manager. His Community responsibilities include assisting users to quantify Kubeflow business value, developing critical user journeys (CUJs), triaging incoming user issues, prioritizing feature delivery, writing release announcements, and delivering Kubeflow presentations and demonstrations.

Mr. Bottum is also a VP of Arrikto. Arrikto simplifies storage operations for stateful Kubernetes applications by enabling efficient local storage architectures with data durability and portability. Arrikto is a core code contributor to Kubeflow.

Join our MLOps community Slack

https://go.mlops.community/slack

Connect with Demetrios on LinkedIn: ⁠https://www.linkedin.com/in/dpbrinkm/

Connect with Josh on LinkedIn:

https://www.linkedin.com/in/joshbottum/